• Journal of Fisheries and Marine Sciences Education
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• v.25 no.2
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• pp.405-409
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• 2013

In this study, the researcher tried to analyse the effects of various palm angles in sculling on the variation of underwater weighting. An experiment was conducted on the study subjects of 14 males with life guard licences issued by the Korean Red Cross, living in B district with their spontaneous consent after explaining the purpose and method of this study sufficiently. The effects of various angles in sculling on underwater weighting is as follows; The underwater weighting in sculling gradually decreased with the increasing angle of the palm from $0^{\circ}$ to $45^{\circ}$ during sculling(p<.001). Overall, it is concluded that the optimal efficiency of sculling can be achieved at the angles $30^{\circ}$ and $45^{\circ}$. But, it is a little limited that we generalize the result drawn from variation of underwater weighting depending on the angles as an actual lift and drag value, which warrants further studies on the measuring of overall swimming movement of rotary kick of our lower body as well as sculling, along with various subjects.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.1
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• pp.18-24
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• 2012

This study analyses the effects of various angles in sculling on human body lift and drag by means of computational fluid dynamics, discusses the importance of sculling and provides a basis for the development of future water safety education programmes. Study subjects were based on the mean data collected from males in the age of 20s from a survey on the anthropometric dimensions of the Koreans. Moreover, lift, drag as well as coefficient values, all of which were governed by the angle of the palm, were calculated using 3-dimentional modelling produced by computational fluid dynamics programmes i.e. CFD. Interpretations were performed via general k-${\varepsilon}$ turbulence modelling in order to determine lift, drag and coefficient values. Turbulence intensity was set to one per cent as per the figures from preceding research papers and 3-dimentional simulations were performed for a total of five different angles $0^{\circ}$, $15^{\circ}$, $30^{\circ}$, $45^{\circ}$ and $60^{\circ}$. The drag and lift values for the differing angles of the hands during sculling movement are as follows. The lift and drag values gradually increased with the increasing angle of the palm, however, the magnitude of increase for drag started to predominate lift from $45^{\circ}$ and lift gradually decreased from $60^{\circ}$. Overall, it is concluded that the optimal efficiency of sculling can be achieved at the angles $15^{\circ}$ and $30^{\circ}$, and it is anticipated that greater safety and informative education can be ensured for Life saving trainees if the results were to be applied to practical settings. However, as the study was conducted using simulation programmes which performed analyses on the collected anthropometric dimension, the obtained results cannot be made universal, which warrants furthers studies involving varied study subjects with actual measurements taken in water.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.31-33
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• 2021

In this paper, a dataset was designed and built to improve the accuracy of finger direction detection using an object detection algorithm based on You Only Look Once (YOLO). In order to improve the object detection performance, about 200 finger image data sets were trained, and to confirm that the detection accuracy differs from each other according to the angle of the palm, 50 comparison groups of different angles were configured and tested. As a result of the experiment, it was confirmed that the detection accuracy of palm located in a direction close to 90° is higher than that of other angles.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.819-822
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• 2016

This paper proposes an algorithm recognizing finger using a distance graph of a detected finger's contour in a depth image. The distance graph shows angles and Euclidean distances between the center of palm and the hand contour as x and y axis respectively. We can obtain hand gestures from the graph using the fact that the graph has local maximum at the positions of finger tips. After we find the center of mass of the wrist using the fingers is thinner than the palm, we make its angle the orienting angle $0^{\circ}$. The simulation results show that the proposed algorithm can detect hand gestures well regardless of the hand direction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1967-1972
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• 2016

This paper defines a distance graph for a hand region in a depth image and proposes an algorithm detecting finger using it. The distance graph is a graph expressing the hand contour with angles and Euclidean distances between the center of palm and the hand contour. Since the distance graph has local maximum at fingertips' position, we can detect finger points and recognize the number of them. The hand contours are always divided into 360 angles and the angles are aligned with the center of the wrist as a starting point. And then the proposed algorithm can well detect fingers without influence of the size and orientation of the hand. Under some limited recognition test conditions, the recognition test's results show that the recognition rate is 100% under 1~3 fingers and 98% under 4~5 fingers and that the failure case can also be recognized by simple conditions to be available to add.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.109-116
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• 1999

Most of carrying works have been performed in almost all production process, conveyer objects, machine equipment and work method. Then, they are made by unfitted design which doesn't consider physical condition of workers. So, it causes them to bring about forceful motion. This study investigated the back power for what difference between lumbar bending angle and back power by selecting 21 male aged 22∼30 years old. Lumbar angle were shared each 30, 60, 90 degrees and measured. The results of this study was that as lumbar angle was increasing, back power was decreased and were very significant differences for angles respectively. Besides. sample correlation coefficients were calculated in order to analyze the relationship between back power, anthropometric dimensions and grip strength. Back power was correlated with weight, arm length, hand length, breadth of palm, breadth of lower arm, breadth of wrist and left-right grip strength.

• Physical Therapy Korea
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• v.27 no.2
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• pp.149-154
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• 2020

Background: The range of pelvic tilt is one of modifiable risk factors in preventing the lower back pain. Objects: The purpose of this study were to compare the range of pelvic tilt motion by testing position and sex. Methods: One hundred five young adults (61 females and 44 males) agreed to participate in measuring the anterior and posterior pelvic tilt with the PALM (Palpation Meter) in sitting and standing. The range of pelvic tilt motion was defined as the difference between the pelvic anterior and posterior tilt angles. Results: In general, the anterior pelvic tilt was greater (p < 0.01) in standing than in sitting and the posterior pelvic tilt was lesser (p < 0.01) in sitting than in standing. The anterior pelvic tilt in sitting and standing was greater (p < 0.01) in the females than in the males. However, the effect of sex on the posterior pelvic tilt was only significant in sitting (p < 0.01), but not in standing (p = 0.78). The range of pelvic tilt was greater (p = 0.03) in sitting but not significantly (p = 0.07) affected by the sex. Conclusion: The pelvic tilt motion in these young adults showed large variability and further studies are needed to understand better its relationship to the prevalence of the lower back disorders.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.157-164
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• 2015

The existing input device is limited to keyboard and mouse. However, recently new type of input device has been developed in response to requests from users. To reflect this trend we propose the new type of input device that gives instruction as analyzing the hand motion of image without special device. After binarizing the skin color area using Cam-Shift method and tracking, it recognizes the hand motion by inputting the finger areas and the angles from the palm center point, which are separated through labeling, into four cardinal directions and counting them. In cases when specific background was not set and without gloves, the recognition rate remained approximately at 75 percent. However, when specific background was set and the person wore red gloves, the recognition rate increased to 90.2 percent due to reduction in noise.